75 search results - page 4 / 15 » Sparse Feature Learning for Deep Belief Networks |
105
Voted
CVPR
2011
IEEE
14 years 5 months ago
2011
IEEE
The most popular way to use probabilistic models in vision is first to extract some descriptors of small image patches or object parts using well-engineered features, and then to...
ICDAR
2007
IEEE
15 years 3 months ago
2007
IEEE
We describe an unsupervised learning algorithm for extracting sparse and locally shift-invariant features. We also devise a principled procedure for learning hierarchies of invari...
NECO
2008
14 years 9 months ago
2008
Deep Belief Networks (DBN) are generative neural network models with many layers of hidden explanatory factors, recently introduced by Hinton et al., along with a greedy layer-wis...
ICPR
2010
IEEE
15 years 25 days ago
2010
IEEE
Ultrasound has become a useful tool for speech scientists studying mechanisms of language sound production. State-of-the-art methods for extracting tongue contours from ultrasound...
85
Voted
INTERSPEECH
2010
14 years 4 months ago
2010
Recently, Deep Belief Networks (DBNs) have been proposed for phone recognition and were found to achieve highly competitive performance. In the original DBNs, only framelevel info...